合并Keras中的变量

Question

我正在用Keras构建一个卷积神经网络，并希望在最后一个完全连接层之前添加一个具有数据标准差的单个节点。

下面是重现错误的最小代码:

from keras.layers import merge, Input, Dense
from keras.layers import Convolution1D, Flatten
from keras import backend as K
input_img = Input(shape=(64, 4))
x = Convolution1D(48, 3, activation='relu', init='he_normal')(input_img)
x = Flatten()(x)
std = K.std(input_img, axis=1)
x = merge([x, std], mode='concat', concat_axis=1)
output =  Dense(100, activation='softmax', init='he_normal')(x)

这导致以下TypeError:

-----------------------------------------------------------------
TypeError                       Traceback (most recent call last)
<ipython-input-117-c1289ebe610e> in <module>()
      6 x = merge([x, std], mode='concat', concat_axis=1)
      7 
----> 8 output =  Dense(100, activation='softmax', init='he_normal')(x)
/home/ubuntu/anaconda2/envs/tensorflow/lib/python2.7/site-packages/keras/engine/topology.pyc in __call__(self, x, mask)
    486                                     '`layer.build(batch_input_shape)`')
    487             if len(input_shapes) == 1:
--> 488                 self.build(input_shapes[0])
    489             else:
    490                 self.build(input_shapes)
/home/ubuntu/anaconda2/envs/tensorflow/lib/python2.7/site-packages/keras/layers/core.pyc in build(self, input_shape)
    701 
    702         self.W = self.init((input_dim, self.output_dim),
--> 703                            name='{}_W'.format(self.name))
    704         if self.bias:
    705             self.b = K.zeros((self.output_dim,),
/home/ubuntu/anaconda2/envs/tensorflow/lib/python2.7/site-packages/keras/initializations.pyc in he_normal(shape, name, dim_ordering)
     64     '''
     65     fan_in, fan_out = get_fans(shape, dim_ordering=dim_ordering)
---> 66     s = np.sqrt(2. / fan_in)
     67     return normal(shape, s, name=name)
     68 
TypeError: unsupported operand type(s) for /: 'float' and 'NoneType'

知道为什么吗?

Answer 1

std不是Keras层，因此它不满足层输入/输出形状接口。解决方案是使用Lambda层包裹K.std:

from keras.layers import merge, Input, Dense, Lambda
from keras.layers import Convolution1D, Flatten
from keras import backend as K
input_img = Input(shape=(64, 4))
x = Convolution1D(48, 3, activation='relu', init='he_normal')(input_img)
x = Flatten()(x)
std = Lambda(lambda x: K.std(x, axis=1))(input_img)
x = merge([x, std], mode='concat', concat_axis=1)
output =  Dense(100, activation='softmax', init='he_normal')(x)